Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "18 April 2023 11 02AM/All"
postexperiment_date <- "18 April 2023 03 47PM/All"

##--- last fish run in trial ---##
experiment_date <- "18 April 2023 12 32PM/Oxygen"
experiment_date2 <- "18 April 2023 12 32PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "18 April 2023 11 12AM/All"
postexperiment_date_asus <- "18 April 2023 05 18PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "18 April 2023 01 59PM/Oxygen"
experiment_date2_asus <- "18 April 2023 01 59PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573+0.00385
chamber3_asus = 0.04551+0.00322
chamber4_asus = 0.04791+0.00277

Date_tested="2023-04-18"
Clutch = "66" 
Male = "CPRE375" 
Female = "CPRE377"
Population = "Pretty patches"
Tank =177 
salinity =36 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0006264
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "18 April 2023 12 00PM/Oxygen"
experiment_mmr_date2 <- "18 April 2023 12 00PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.001363504

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] 0.0005285563

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  7  9 11 12 13 15 16 17 18 19 20 21 23 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   9    1     299.0554 -0.02024314 0.993      NA 3905   4137  9910.72
## 2:  11    1     323.8429 -0.02051141 0.994      NA 4889   5123 10990.62
## 3:  18    1     366.4331 -0.01810617 0.993      NA 8348   8581 14770.40
## 4:  19    1     341.9400 -0.01584166 0.985      NA 8823   9053 15310.40
## 5:  20    1     342.3057 -0.01533166 0.980      NA 9313   9547 15850.18
## 6:  21    1     361.5652 -0.01600495 0.978      NA 9807  10041 16389.93
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10165.50 98.239 93.048 -0.02024314 0.0009372074   -0.02118035 -0.02118035
## 2: 11245.93 98.164 92.894 -0.02051141 0.0008473965   -0.02135881 -0.02135881
## 3: 15025.41 98.681 94.131 -0.01810617 0.0005331372   -0.01863931 -0.01863931
## 4: 15565.77 99.031 95.113 -0.01584166 0.0004882237   -0.01632988 -0.01632988
## 5: 16105.82 98.906 95.071 -0.01533166 0.0004433322   -0.01577499 -0.01577499
## 6: 16645.54 98.825 94.849 -0.01600495 0.0003984556   -0.01640340 -0.01640340
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.2405657
## 2:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.2425927
## 3:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.2117047
## 4:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.1854743
## 5:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.1791719
## 6:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.1863093
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -384.0449          NA  mgO2/hr/kg   -384.0449
## 2:   -387.2808          NA  mgO2/hr/kg   -387.2808
## 3:   -337.9705          NA  mgO2/hr/kg   -337.9705
## 4:   -296.0957          NA  mgO2/hr/kg   -296.0957
## 5:   -286.0343          NA  mgO2/hr/kg   -286.0343
## 6:   -297.4287          NA  mgO2/hr/kg   -297.4287
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 1 CPRE375 CPRE377 Pretty patches 177 0.0006264 ch4 Dell 0.0486 2023-04-18 2024-06-20 good/good 36 27 340.5641 0.2133294 0.9886

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.65
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 14 15 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.36
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     278.4250 -0.04882027 0.9980777      NA 185    239 3812.77
##   2:  NA    2     278.1839 -0.04875727 0.9980229      NA 186    240 3813.86
##   3:  NA    3     278.0948 -0.04873236 0.9980909      NA 190    244 3818.59
##   4:  NA    4     278.0541 -0.04872400 0.9979924      NA 184    238 3811.68
##   5:  NA    5     277.9176 -0.04868695 0.9979982      NA 189    243 3817.48
##  ---                                                                       
## 211:  NA  211     215.8022 -0.03190956 0.9666675      NA   5     59 3611.49
## 212:  NA  212     212.8276 -0.03109284 0.9638216      NA   4     58 3610.39
## 213:  NA  213     209.6830 -0.03022921 0.9602913      NA   3     57 3609.23
## 214:  NA  214     206.3677 -0.02931816 0.9577790      NA   2     56 3608.14
## 215:  NA  215     203.5709 -0.02854949 0.9546363      NA   1     55 3607.06
##      endtime     oxy endoxy        rate
##   1: 3872.77  92.244 89.359 -0.04882027
##   2: 3873.86  92.237 89.349 -0.04875727
##   3: 3878.59  91.952 89.137 -0.04873236
##   4: 3871.68  92.293 89.384 -0.04872400
##   5: 3877.48  92.027 89.168 -0.04868695
##  ---                                   
## 211: 3671.49 100.330 98.456 -0.03190956
## 212: 3670.39 100.320 98.506 -0.03109284
## 213: 3669.23 100.340 98.530 -0.03022921
## 214: 3668.14 100.370 98.641 -0.02931816
## 215: 3667.06 100.390 98.681 -0.02854949
## 
## Regressions : 215 | Results : 215 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 215 adjusted rate(s):
## Rate          : -0.04882027
## Adjustment    : 0.001363504
## Adjusted Rate : -0.05018378 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 215 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 214 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1      278.425 -0.04882027 0.9980777      NA 185    239 3812.77
##    endtime    oxy endoxy        rate  adjustment rate.adjusted  rate.input
## 1: 3872.77 92.244 89.359 -0.04882027 0.001363504   -0.05018378 -0.05018378
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0006264   NA 36 27 1.013253 -0.5699859
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -909.9391          NA  mgO2/hr/kg   -909.9391
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 1 CPRE375 CPRE377 Pretty patches 177 0.0006264 ch4 Dell 0.0486 2023-04-18 2024-06-20 good/good 36 27 340.5641 0.2133294 0.9886 909.9391 0.5699859 0.9980777 569.375 0.3566565

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 222 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Replicate = 2 
mass = 0.0006220 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes="check max"

##--- time of trail ---## 
experiment_mmr_date <- "18 April 2023 12 11PM/Oxygen"
experiment_mmr_date2 <- "18 April 2023 12 11PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.000591797

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.0006373406

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  7  9 11 12 13 15 16 17 18 19 20 21 23 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   9    1     266.2786 -0.01685612 0.989      NA 3905   4137  9910.72
## 2:  14    1     310.6974 -0.01676364 0.995      NA 6371   6605 12610.56
## 3:  18    1     355.0012 -0.01730287 0.996      NA 8348   8581 14770.40
## 4:  19    1     360.7695 -0.01705448 0.993      NA 8823   9053 15310.40
## 5:  20    1     367.1801 -0.01689619 0.996      NA 9313   9547 15850.18
## 6:  21    1     374.6310 -0.01679088 0.995      NA 9807  10041 16389.93
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 10165.50 98.978 94.687 -0.01685612 -0.0006150500   -0.01624107 -0.01624107
## 2: 12866.10 99.031 94.828 -0.01676364 -0.0006272964   -0.01613634 -0.01613634
## 3: 15025.41 99.183 94.871 -0.01730287 -0.0006370907   -0.01666578 -0.01666578
## 4: 15565.77 99.407 95.118 -0.01705448 -0.0006395406   -0.01641494 -0.01641494
## 5: 16105.82 99.220 94.895 -0.01689619 -0.0006419893   -0.01625420 -0.01625420
## 6: 16645.54 99.157 94.937 -0.01679088 -0.0006444371   -0.01614644 -0.01614644
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.1889064
## 2:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.1876883
## 3:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.1938464
## 4:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.1909288
## 5:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.1890591
## 6:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.1878058
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -303.7080          NA  mgO2/hr/kg   -303.7080
## 2:   -301.7497          NA  mgO2/hr/kg   -301.7497
## 3:   -311.6502          NA  mgO2/hr/kg   -311.6502
## 4:   -306.9595          NA  mgO2/hr/kg   -306.9595
## 5:   -303.9536          NA  mgO2/hr/kg   -303.9536
## 6:   -301.9386          NA  mgO2/hr/kg   -301.9386
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 2 CPRE375 CPRE377 Pretty patches 177 0.000622 ch3 Dell 0.04977 2023-04-18 2024-06-20 good/good 36 27 305.642 0.1901093 0.9938

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.65
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,  
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  3  5  6  7  8  9 11 12 13 15 16 17 18 19 20 21 22 24 25
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.39
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     253.5037 -0.03531319 0.9928175      NA 177    230 4469.72
##   2:  NA    2     253.4582 -0.03530410 0.9927685      NA 176    229 4468.64
##   3:  NA    3     253.3976 -0.03528878 0.9926816      NA 178    231 4470.83
##   4:  NA    4     252.6254 -0.03511640 0.9917127      NA 179    232 4471.92
##   5:  NA    5     252.5656 -0.03510672 0.9913971      NA 175    228 4467.55
##  ---                                                                       
## 206:  NA  206     159.2923 -0.01381036 0.9770971      NA   9     62 4276.45
## 207:  NA  207     159.2317 -0.01379609 0.9764193      NA   3     56 4269.76
## 208:  NA  208     159.2100 -0.01379096 0.9772299      NA   8     61 4275.36
## 209:  NA  209     158.7150 -0.01367600 0.9756495      NA   2     55 4268.68
## 210:  NA  210     157.9931 -0.01350829 0.9735301      NA   1     54 4267.45
##      endtime     oxy endoxy        rate
##   1: 4529.72  95.528 93.658 -0.03531319
##   2: 4528.64  95.552 93.651 -0.03530410
##   3: 4530.83  95.553 93.614 -0.03528878
##   4: 4531.92  95.534 93.619 -0.03511640
##   5: 4527.55  95.583 93.639 -0.03510672
##  ---                                   
## 206: 4336.45 100.260 99.370 -0.01381036
## 207: 4329.76 100.290 99.477 -0.01379609
## 208: 4335.36 100.270 99.413 -0.01379096
## 209: 4328.68 100.270 99.500 -0.01367600
## 210: 4327.45 100.290 99.494 -0.01350829
## 
## Regressions : 210 | Results : 210 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 210 adjusted rate(s):
## Rate          : -0.03531319
## Adjustment    : -0.000591797
## Adjusted Rate : -0.03472139 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 210 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 209 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     253.5037 -0.03531319 0.9928175      NA 177    230 4469.72
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 4529.72 95.528 93.658 -0.03531319 -0.000591797   -0.03472139 -0.03472139
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.000622   NA 36 27 1.013253 -0.4038585
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -649.2902          NA  mgO2/hr/kg   -649.2902
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 2 CPRE375 CPRE377 Pretty patches 177 0.000622 ch3 Dell 0.04977 2023-04-18 2024-06-20 good/good 36 27 305.642 0.1901093 0.9938 649.2902 0.4038585 0.9928175 343.6483 0.2137492 check max

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 223 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0006914 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "18 April 2023 12 21PM/Oxygen"
experiment_mmr_date2 <- "18 April 2023 12 21PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.0001139865

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.0006262587

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  7  9 11 12 13 15 16 17 18 19 20 21 23 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  14    1     311.1846 -0.01673173 0.989      NA 6371   6605 12610.56
## 2:  15    1     329.6496 -0.01743425 0.979      NA 6865   7099 13150.65
## 3:  16    1     326.6924 -0.01655927 0.991      NA 7359   7592 13689.96
## 4:  17    1     347.6085 -0.01738690 0.984      NA 7854   8088 14230.91
## 5:  18    1     373.9046 -0.01851675 0.975      NA 8348   8581 14770.40
## 6:  19    1     374.7739 -0.01791541 0.982      NA 8823   9053 15310.40
##     endtime     oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 12866.10  99.883 95.652 -0.01673173 -0.0004630045   -0.01626873 -0.01626873
## 2: 13406.11 100.020 95.643 -0.01743425 -0.0005028142   -0.01693144 -0.01693144
## 3: 13944.58  99.789 95.616 -0.01655927 -0.0005425385   -0.01601673 -0.01601673
## 4: 14486.23  99.820 95.512 -0.01738690 -0.0005824403   -0.01680446 -0.01680446
## 5: 15025.41  99.971 95.314 -0.01851675 -0.0006221974   -0.01789455 -0.01789455
## 6: 15565.77 100.010 95.473 -0.01791541 -0.0006620167   -0.01725340 -0.01725340
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.1746283
## 2:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.1817418
## 3:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.1719233
## 4:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.1803788
## 5:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.1920799
## 6:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.1851977
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -252.5720          NA  mgO2/hr/kg   -252.5720
## 2:   -262.8606          NA  mgO2/hr/kg   -262.8606
## 3:   -248.6597          NA  mgO2/hr/kg   -248.6597
## 4:   -260.8892          NA  mgO2/hr/kg   -260.8892
## 5:   -277.8129          NA  mgO2/hr/kg   -277.8129
## 6:   -267.8589          NA  mgO2/hr/kg   -267.8589
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 3 CPRE375 CPRE377 Pretty patches 177 0.0006914 ch2 Dell 0.04593 2023-04-18 2024-06-20 good/good 36 27 264.3987 0.1828053 0.9818

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.65
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  7  8  9 10 11 12 13 15 16 17 19 20 21 22 23 25
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.57
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     344.8832 -0.04894462 0.9961148      NA 103    156 5035.75
##   2:  NA    2     344.8509 -0.04893862 0.9961035      NA 102    155 5034.60
##   3:  NA    3     344.4105 -0.04885221 0.9959082      NA 101    154 5033.51
##   4:  NA    4     344.3348 -0.04883600 0.9959517      NA 104    157 5036.83
##   5:  NA    5     343.8748 -0.04874686 0.9957113      NA 100    153 5032.30
##  ---                                                                       
## 207:  NA  207     180.0756 -0.01603501 0.9555944      NA   5     58 4924.31
## 208:  NA  208     177.9715 -0.01561082 0.9464964      NA   4     57 4923.16
## 209:  NA  209     175.4175 -0.01509590 0.9310876      NA   3     56 4922.07
## 210:  NA  210     173.0812 -0.01462451 0.9205597      NA   2     55 4920.97
## 211:  NA  211     170.0709 -0.01401677 0.9079978      NA   1     54 4919.88
##      endtime     oxy  endoxy        rate
##   1: 5095.75  98.409  95.522 -0.04894462
##   2: 5094.60  98.413  95.580 -0.04893862
##   3: 5093.51  98.471  95.613 -0.04885221
##   4: 5096.83  98.389  95.502 -0.04883600
##   5: 5092.30  98.492  95.711 -0.04874686
##  ---                                    
## 207: 4984.31 100.930 100.110 -0.01603501
## 208: 4983.16 100.880 100.140 -0.01561082
## 209: 4982.07 100.930 100.140 -0.01509590
## 210: 4980.97 100.910 100.120 -0.01462451
## 211: 4979.88 100.940 100.180 -0.01401677
## 
## Regressions : 211 | Results : 211 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 211 adjusted rate(s):
## Rate          : -0.04894462
## Adjustment    : 0.0001139865
## Adjusted Rate : -0.0490586 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 2 rate(s) removed, 209 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 208 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     344.8832 -0.04894462 0.9961148      NA 103    156 5035.75
##    endtime    oxy endoxy        rate   adjustment rate.adjusted rate.input
## 1: 5095.75 98.409 95.522 -0.04894462 0.0001139865    -0.0490586 -0.0490586
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0006914   NA 36 27 1.013253 -0.5265943
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -761.6347          NA  mgO2/hr/kg   -761.6347
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 3 CPRE375 CPRE377 Pretty patches 177 0.0006914 ch2 Dell 0.04593 2023-04-18 2024-06-20 good/good 36 27 264.3987 0.1828053 0.9818 761.6347 0.5265943 0.9961148 497.236 0.343789

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 224 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0004850
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "18 April 2023 12 32PM/Oxygen"
experiment_mmr_date2 <- "18 April 2023 12 32PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.0003219256

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.001299983

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  7  9 11 12 13 15 16 17 18 19 20 21 23 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   9    1     298.0006 -0.02005874 0.998      NA 3905   4137  9910.72
## 2:  14    1     354.9520 -0.02028034 0.999      NA 6371   6605 12610.56
## 3:  16    1     365.4149 -0.01946001 0.999      NA 7359   7592 13689.96
## 4:  18    1     404.4754 -0.02065853 0.999      NA 8348   8581 14770.40
## 5:  20    1     409.2046 -0.01954551 0.999      NA 9313   9547 15850.18
## 6:  21    1     422.7582 -0.01971872 0.999      NA 9807  10041 16389.93
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 10165.50 99.032 93.946 -0.02005874 -0.0008212892   -0.01923745 -0.01923745
## 2: 12866.10 99.086 93.989 -0.02028034 -0.0010842818   -0.01919606 -0.01919606
## 3: 13944.58 99.040 94.032 -0.01946001 -0.0011893670   -0.01827065 -0.01827065
## 4: 15025.41 99.293 94.000 -0.02065853 -0.0012946173   -0.01936392 -0.01936392
## 5: 16105.82 99.298 94.398 -0.01954551 -0.0013998150   -0.01814570 -0.01814570
## 6: 16645.54 99.426 94.494 -0.01971872 -0.0014523834   -0.01826634 -0.01826634
##    oxy.unit time.unit volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.2090571
## 2:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.2086073
## 3:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.1985507
## 4:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.2104314
## 5:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.1971928
## 6:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.1985038
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -431.0455          NA  mgO2/hr/kg   -431.0455
## 2:   -430.1182          NA  mgO2/hr/kg   -430.1182
## 3:   -409.3828          NA  mgO2/hr/kg   -409.3828
## 4:   -433.8792          NA  mgO2/hr/kg   -433.8792
## 5:   -406.5831          NA  mgO2/hr/kg   -406.5831
## 6:   -409.2862          NA  mgO2/hr/kg   -409.2862
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 4 CPRE375 CPRE377 Pretty patches 177 0.000485 ch1 Dell 0.0465 2023-04-18 2024-06-20 good/good 36 27 422.7424 0.2050301 0.9988

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 2.38
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  7  9 11 12 13 15 16 17 18 19 20 21 23 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.38
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     393.1080 -0.05236196 0.9972968      NA 110    165 5669.37
##   2:  NA    2     393.0035 -0.05234488 0.9972576      NA 108    163 5667.07
##   3:  NA    3     392.9373 -0.05233262 0.9972273      NA 109    164 5668.28
##   4:  NA    4     392.1649 -0.05219581 0.9968522      NA 111    166 5670.48
##   5:  NA    5     392.1283 -0.05219175 0.9969575      NA 107    162 5665.97
##  ---                                                                       
## 210:  NA  210     259.0884 -0.02854896 0.9290534      NA   5     60 5550.57
## 211:  NA  211     254.8030 -0.02778121 0.9240469      NA   4     59 5549.48
## 212:  NA  212     250.8035 -0.02706450 0.9195244      NA   3     58 5548.40
## 213:  NA  213     246.2340 -0.02624550 0.9133548      NA   2     57 5547.31
## 214:  NA  214     241.3324 -0.02536679 0.9064877      NA   1     56 5546.22
##      endtime     oxy endoxy        rate
##   1: 5729.37  96.263 93.189 -0.05236196
##   2: 5727.07  96.383 93.258 -0.05234488
##   3: 5728.28  96.321 93.259 -0.05233262
##   4: 5730.48  96.236 93.204 -0.05219581
##   5: 5725.97  96.412 93.335 -0.05219175
##  ---                                   
## 210: 5610.57 100.390 98.738 -0.02854896
## 211: 5609.48 100.420 98.794 -0.02778121
## 212: 5608.40 100.390 98.826 -0.02706450
## 213: 5607.31 100.380 98.857 -0.02624550
## 214: 5606.22 100.380 98.909 -0.02536679
## 
## Regressions : 214 | Results : 214 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 214 adjusted rate(s):
## Rate          : -0.05236196
## Adjustment    : -0.0003219256
## Adjusted Rate : -0.05204004 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 5 rate(s) removed, 209 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 208 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1      393.108 -0.05236196 0.9972968      NA 110    165 5669.37
##    endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 5729.37 96.263 93.189 -0.05236196 -0.0003219256   -0.05204004 -0.05204004
##    oxy.unit time.unit volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.000485   NA 36 27 1.013253 -0.5655292
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1166.04          NA  mgO2/hr/kg    -1166.04
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 4 CPRE375 CPRE377 Pretty patches 177 0.000485 ch1 Dell 0.0465 2023-04-18 2024-06-20 good/good 36 27 422.7424 0.2050301 0.9988 1166.04 0.5655292 0.9972968 743.2972 0.3604992

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 225 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0005703 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes="check max; second cycle used for max"

##--- time of trail ---## 
experiment_mmr_date_asus <- "18 April 2023 01 41PM/Oxygen"
experiment_mmr_date2_asus <- "18 April 2023 01 41PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.0004354517

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.001956839

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 4.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 4.53
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 6 rate(s) removed, 15 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 9 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   7    1     245.1459 -0.01096094 0.976      NA 2293   2479 13422.37
## 2:  12    1     369.5385 -0.01682204 0.982      NA 4258   4444 16121.66
## 3:  17    1     435.8368 -0.01792602 0.990      NA 6229   6415 18821.92
## 4:  19    1     476.3644 -0.01905116 0.971      NA 7017   7203 19902.15
## 5:  20    1     495.4453 -0.01941515 0.978      NA 7411   7597 20442.10
## 6:  21    1     470.7038 -0.01771569 0.977      NA 7804   7990 20981.60
##     endtime    oxy endoxy        rate   adjustment rate.adjusted   rate.input
## 1: 13677.22 97.801 95.363 -0.01096094 -0.001419904  -0.009541033 -0.009541033
## 2: 16376.25 98.060 94.274 -0.01682204 -0.002227657  -0.014594382 -0.014594382
## 3: 19076.11 98.262 93.751 -0.01792602 -0.003035679  -0.014890338 -0.014890338
## 4: 20157.37 97.622 92.462 -0.01905116 -0.003359104  -0.015692059 -0.015692059
## 5: 20696.97 98.027 93.401 -0.01941515 -0.003520637  -0.015894517 -0.015894517
## 6: 21236.45 98.523 94.187 -0.01771569 -0.003682086  -0.014033604 -0.014033604
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.1130047
## 2:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.1728569
## 3:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.1763623
## 4:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.1858579
## 5:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.1882558
## 6:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.1662150
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -198.1496          NA  mgO2/hr/kg   -198.1496
## 2:   -303.0982          NA  mgO2/hr/kg   -303.0982
## 3:   -309.2447          NA  mgO2/hr/kg   -309.2447
## 4:   -325.8950          NA  mgO2/hr/kg   -325.8950
## 5:   -330.0996          NA  mgO2/hr/kg   -330.0996
## 6:   -291.4519          NA  mgO2/hr/kg   -291.4519
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 5 CPRE375 CPRE377 Pretty patches 177 0.0005703 ch4 Asus 0.05068 2023-04-18 2024-06-20 good/good 36 27 311.9579 0.1779096 0.9796

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 3.57
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.89
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     651.0906 -0.06075869 0.9485473      NA  52     96 9109.33
##   2:  NA    2     649.7777 -0.06061366 0.9476698      NA  53     97 9110.68
##   3:  NA    3     646.3363 -0.06023977 0.9454619      NA  51     95 9107.98
##   4:  NA    4     645.6652 -0.06016236 0.9448084      NA  54     98 9112.07
##   5:  NA    5     641.9835 -0.05976492 0.9422267      NA  50     94 9106.63
##  ---                                                                       
## 171:  NA  171     258.0395 -0.01764518 0.9471145      NA  14     58 9057.06
## 172:  NA  172     257.5374 -0.01758894 0.9485654      NA  18     62 9062.52
## 173:  NA  173     256.0793 -0.01742936 0.9447321      NA  15     59 9058.44
## 174:  NA  174     255.0681 -0.01731777 0.9427337      NA  17     61 9061.17
## 175:  NA  175     254.5536 -0.01726129 0.9421690      NA  16     60 9059.82
##      endtime    oxy endoxy        rate
##   1: 9169.33 97.261 94.123 -0.06075869
##   2: 9170.68 97.326 94.212 -0.06061366
##   3: 9167.98 97.283 94.257 -0.06023977
##   4: 9172.07 97.303 94.228 -0.06016236
##   5: 9166.63 97.370 94.374 -0.05976492
##  ---                                  
## 171: 9117.06 98.277 97.172 -0.01764518
## 172: 9122.52 97.998 97.105 -0.01758894
## 173: 9118.44 98.231 97.221 -0.01742936
## 174: 9121.17 98.122 97.121 -0.01731777
## 175: 9119.82 98.173 97.213 -0.01726129
## 
## Regressions : 175 | Results : 175 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 175 adjusted rate(s):
## Rate          : -0.06075869
## Adjustment    : 0.0004354517
## Adjusted Rate : -0.06119414 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 57 rate(s) removed, 118 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 117 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     651.0906 -0.06075869 0.9485473      NA  52     96 9109.33
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 9169.33 97.261 94.123 -0.06075869 0.0004354517   -0.06119414 -0.06119414
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.05068 0.0005703   NA 36 27 1.013253 -0.7247879
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1270.889          NA  mgO2/hr/kg   -1270.889
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 5 CPRE375 CPRE377 Pretty patches 177 0.0005703 ch4 Asus 0.05068 2023-04-18 2024-06-20 good/good 36 27 311.9579 0.1779096 0.9796 1270.889 0.7247879 0.9485473 958.931 0.5468783 check max; second cycle used for max
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 226 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0005827
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "18 April 2023 01 41PM/Oxygen"
experiment_mmr_date2_asus <- "18 April 2023 01 41PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001226408

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.002626371

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 4.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 4.53
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 5 rate(s) removed, 16 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 10 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  12    1     479.8618 -0.02367278 0.994      NA 4258   4437 16121.66
## 2:  14    1     420.0917 -0.01877637 0.960      NA 5047   5226 17202.69
## 3:  16    1     420.6709 -0.01769962 0.976      NA 5835   6014 18282.67
## 4:  17    1     519.6956 -0.02238096 0.991      NA 6229   6408 18821.92
## 5:  20    1     477.7474 -0.01853997 0.995      NA 7411   7590 20442.10
## 6:  21    1     573.2401 -0.02261859 0.999      NA 7804   7983 20981.60
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 16366.68 98.161 92.208 -0.02367278 -0.002784015   -0.02088877 -0.02088877
## 2: 17447.82 97.705 92.590 -0.01877637 -0.002973342   -0.01580303 -0.01580303
## 3: 18527.49 97.794 92.595 -0.01769962 -0.003162448   -0.01453718 -0.01453718
## 4: 19066.55 98.209 92.901 -0.02238096 -0.003256868   -0.01912410 -0.01912410
## 5: 20687.12 98.508 94.128 -0.01853997 -0.003540639   -0.01499933 -0.01499933
## 6: 21226.89 98.729 93.246 -0.02261859 -0.003635144   -0.01898345 -0.01898345
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.2378887
## 2:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.1799705
## 3:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.1655545
## 4:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.2177919
## 5:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.1708176
## 6:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.2161902
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -408.2524          NA  mgO2/hr/kg   -408.2524
## 2:   -308.8562          NA  mgO2/hr/kg   -308.8562
## 3:   -284.1162          NA  mgO2/hr/kg   -284.1162
## 4:   -373.7634          NA  mgO2/hr/kg   -373.7634
## 5:   -293.1485          NA  mgO2/hr/kg   -293.1485
## 6:   -371.0146          NA  mgO2/hr/kg   -371.0146
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 6 CPRE375 CPRE377 Pretty patches 177 0.0005827 ch3 Asus 0.04873 2023-04-18 2024-06-20 good/good 36 27 351.007 0.2045318 0.9878

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 3.57
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.89
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     774.8067 -0.07494127 0.9972187      NA   7     51 9047.44
##   2:  NA    2     774.6271 -0.07492150 0.9972181      NA   6     50 9046.07
##   3:  NA    3     773.5671 -0.07480499 0.9971317      NA   5     49 9044.71
##   4:  NA    4     773.2386 -0.07476855 0.9972072      NA   8     52 9048.79
##   5:  NA    5     770.8049 -0.07450119 0.9966799      NA   4     48 9043.34
##  ---                                                                       
## 171:  NA  171     261.5958 -0.01907512 0.9082224      NA 157    201 9253.93
## 172:  NA  172     255.7552 -0.01844513 0.8844449      NA 158    202 9255.27
## 173:  NA  173     253.8804 -0.01824263 0.8623747      NA 161    205 9259.41
## 174:  NA  174     251.2485 -0.01795909 0.8679038      NA 159    203 9256.65
## 175:  NA  175     249.0783 -0.01772516 0.8632020      NA 160    204 9258.04
##      endtime    oxy endoxy        rate
##   1: 9107.44 96.817 92.282 -0.07494127
##   2: 9106.07 96.838 92.419 -0.07492150
##   3: 9104.71 96.825 92.543 -0.07480499
##   4: 9108.79 96.759 92.222 -0.07476855
##   5: 9103.34 96.932 92.637 -0.07450119
##  ---                                  
## 171: 9313.93 84.985 84.172 -0.01907512
## 172: 9315.27 85.010 84.247 -0.01844513
## 173: 9319.41 84.960 83.665 -0.01824263
## 174: 9316.65 84.984 84.175 -0.01795909
## 175: 9318.04 84.995 84.013 -0.01772516
## 
## Regressions : 175 | Results : 175 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 175 adjusted rate(s):
## Rate          : -0.07494127
## Adjustment    : -0.001226408
## Adjusted Rate : -0.07371486 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 72 rate(s) removed, 103 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 102 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     774.8067 -0.07494127 0.9972187      NA   7     51 9047.44
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 9107.44 96.817 92.282 -0.07494127 -0.001226408   -0.07371486 -0.07371486
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04873 0.0005827   NA 36 27 1.013253 -0.8394908
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1440.691          NA  mgO2/hr/kg   -1440.691
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 6 CPRE375 CPRE377 Pretty patches 177 0.0005827 ch3 Asus 0.04873 2023-04-18 2024-06-20 good/good 36 27 351.007 0.2045318 0.9878 1440.691 0.8394908 0.9972187 1089.684 0.634959
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 227 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0003691 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes="second cycle used for max"

##--- time of trail ---## 
experiment_mmr_date_asus <- "18 April 2023 01 59PM/Oxygen"
experiment_mmr_date2_asus <- "18 April 2023 01 59PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.0008138725

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.001699106

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 4.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 4.53
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0     slope_b1   rsq density  row endrow     time
## 1:   4    1     234.2931 -0.011446025 0.972      NA 1213   1391 11801.61
## 2:   5    1     212.4301 -0.009178095 0.966      NA 1606   1785 12341.52
## 3:  12    1     251.5335 -0.009419541 0.964      NA 4258   4437 16121.66
## 4:  13    1     259.8540 -0.009622037 0.989      NA 4653   4831 16662.60
## 5:  17    1     349.3846 -0.013239076 0.966      NA 6229   6408 18821.92
## 6:  20    1     361.7954 -0.012825963 0.985      NA 7411   7590 20442.10
##     endtime     oxy endoxy         rate    adjustment rate.adjusted
## 1: 12046.56  99.311 96.139 -0.011446025 -0.0006240284  -0.010821997
## 2: 12586.40  99.260 97.266 -0.009178095 -0.0007937425  -0.008384353
## 3: 16366.68  99.673 97.094 -0.009419541 -0.0019820822  -0.007437458
## 4: 16906.90  99.610 97.072 -0.009622037 -0.0021520180  -0.007470019
## 5: 19066.55 100.040 96.465 -0.013239076 -0.0028308697  -0.010408207
## 6: 20687.12  99.276 96.255 -0.012825963 -0.0033402477  -0.009485716
##      rate.input oxy.unit time.unit  volume      mass area  S  t        P
## 1: -0.010821997     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253
## 2: -0.008384353     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253
## 3: -0.007437458     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253
## 4: -0.007470019     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253
## 5: -0.010408207     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253
## 6: -0.009485716     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253
##       rate.abs rate.m.spec rate.a.spec output.unit rate.output
## 1: -0.12539447   -339.7304          NA  mgO2/hr/kg   -339.7304
## 2: -0.09714949   -263.2064          NA  mgO2/hr/kg   -263.2064
## 3: -0.08617783   -233.4810          NA  mgO2/hr/kg   -233.4810
## 4: -0.08655511   -234.5031          NA  mgO2/hr/kg   -234.5031
## 5: -0.12059989   -326.7404          NA  mgO2/hr/kg   -326.7404
## 6: -0.10991099   -297.7810          NA  mgO2/hr/kg   -297.7810
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 7 CPRE375 CPRE377 Pretty patches 177 0.0003691 ch2 Asus 0.04958 2023-04-18 2024-06-20 good/good 36 27 292.3923 0.107922 0.9756

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 4.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  8  9 10 11 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.60
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow     time
##   1:  NA    1     329.1448 -0.02258581 0.9945504      NA  79    123 10244.42
##   2:  NA    2     329.0001 -0.02257119 0.9944607      NA  81    125 10247.39
##   3:  NA    3     328.9215 -0.02256382 0.9943851      NA  80    124 10246.01
##   4:  NA    4     328.8328 -0.02255464 0.9943312      NA  82    126 10248.78
##   5:  NA    5     328.4410 -0.02251637 0.9941417      NA  83    127 10250.14
##  ---                                                                        
## 172:  NA  172     279.2163 -0.01773990 0.9923539      NA 147    191 10337.90
## 173:  NA  173     278.7068 -0.01769003 0.9920142      NA 143    187 10332.41
## 174:  NA  174     278.5890 -0.01767924 0.9927566      NA 146    190 10336.53
## 175:  NA  175     277.7225 -0.01759551 0.9932495      NA 145    189 10335.13
## 176:  NA  176     277.6238 -0.01758581 0.9933259      NA 144    188 10333.78
##       endtime    oxy endoxy        rate
##   1: 10304.42 97.686 96.437 -0.02258581
##   2: 10307.39 97.650 96.410 -0.02257119
##   3: 10306.01 97.690 96.440 -0.02256382
##   4: 10308.78 97.627 96.387 -0.02255464
##   5: 10310.14 97.630 96.337 -0.02251637
##  ---                                   
## 172: 10397.90 95.848 94.711 -0.01773990
## 173: 10392.41 96.024 94.816 -0.01769003
## 174: 10396.53 95.862 94.738 -0.01767924
## 175: 10395.13 95.912 94.773 -0.01759551
## 176: 10393.78 95.988 94.800 -0.01758581
## 
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 176 adjusted rate(s):
## Rate          : -0.02258581
## Adjustment    : 0.0008138725
## Adjusted Rate : -0.02339968 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 176 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 175 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow     time
## 1:  NA    1     329.1448 -0.02258581 0.9945504      NA  79    123 10244.42
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10304.42 97.686 96.437 -0.02258581 0.0008138725   -0.02339968 -0.02339968
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04958 0.0003691   NA 36 27 1.013253 -0.2711321
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -734.5764          NA  mgO2/hr/kg   -734.5764
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 7 CPRE375 CPRE377 Pretty patches 177 0.0003691 ch2 Asus 0.04958 2023-04-18 2024-06-20 good/good 36 27 292.3923 0.107922 0.9756 734.5764 0.2711321 0.9945504 442.1841 0.1632101 second cycle used for max
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 228 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0005120 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "18 April 2023 01 59PM/Oxygen"
experiment_mmr_date2_asus <- "18 April 2023 01 59PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_4.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001068629

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_4.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), # CUSTOM
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.001735673

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 4.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 4.53
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   2    1     321.6209 -0.02083024 0.952      NA  427    605 10722.04
## 2:   5    1     356.9674 -0.02090565 0.989      NA 1606   1785 12341.52
## 3:   6    1     323.9001 -0.01734567 0.967      NA 1940   2078 12882.63
## 4:   7    1     367.9651 -0.01999881 0.993      NA 2293   2472 13422.37
## 5:   8    1     380.8639 -0.02015457 0.993      NA 2686   2864 13961.75
## 6:  11    1     444.4836 -0.02216514 0.994      NA 3864   4043 15582.45
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10967.26 97.995 92.352 -0.02083024 -0.001360234   -0.01947001 -0.01947001
## 2: 12586.40 98.485 93.646 -0.02090565 -0.001495354   -0.01941030 -0.01941030
## 3: 13127.63 99.952 95.816 -0.01734567 -0.001540511   -0.01580516 -0.01580516
## 4: 13667.64 99.382 94.475 -0.01999881 -0.001585559   -0.01841325 -0.01841325
## 5: 14205.65 99.121 94.439 -0.02015457 -0.001630510   -0.01852406 -0.01852406
## 6: 15827.50 98.954 93.561 -0.02216514 -0.001765794   -0.02039934 -0.02039934
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.2077167
## 2:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.2070797
## 3:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.1686181
## 4:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.1964426
## 5:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.1976248
## 6:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.2176313
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -405.6967          NA  mgO2/hr/kg   -405.6967
## 2:   -404.4525          NA  mgO2/hr/kg   -404.4525
## 3:   -329.3322          NA  mgO2/hr/kg   -329.3322
## 4:   -383.6770          NA  mgO2/hr/kg   -383.6770
## 5:   -385.9859          NA  mgO2/hr/kg   -385.9859
## 6:   -425.0612          NA  mgO2/hr/kg   -425.0612
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
66 8 CPRE375 CPRE377 Pretty patches 177 0.000512 ch1 Asus 0.04565 2023-04-18 2024-06-20 good/good 36 27 400.9746 0.205299 0.9842

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 4.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  8  9 10 11 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.60
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow     time
##   1:  NA    1     559.3172 -0.04511041 0.9980922      NA 166    210 10363.91
##   2:  NA    2     558.9421 -0.04507434 0.9980932      NA 167    211 10365.28
##   3:  NA    3     558.8581 -0.04506650 0.9981806      NA 171    215 10370.76
##   4:  NA    4     558.4153 -0.04502403 0.9981418      NA 170    214 10369.39
##   5:  NA    5     558.1957 -0.04500271 0.9979204      NA 165    209 10362.52
##  ---                                                                        
## 172:  NA  172     430.3632 -0.03256180 0.9892361      NA   5     49 10143.01
## 173:  NA  173     424.6576 -0.03200145 0.9847183      NA   4     48 10141.66
## 174:  NA  174     418.5277 -0.03139937 0.9792123      NA   3     47 10140.28
## 175:  NA  175     413.6039 -0.03091570 0.9751180      NA   2     46 10138.90
## 176:  NA  176     408.5721 -0.03042133 0.9709243      NA   1     45 10137.53
##       endtime    oxy endoxy        rate
##   1: 10423.91 91.733 89.113 -0.04511041
##   2: 10425.28 91.754 89.034 -0.04507434
##   3: 10430.76 91.458 88.796 -0.04506650
##   4: 10429.39 91.559 88.823 -0.04502403
##   5: 10422.52 91.825 89.147 -0.04500271
##  ---                                   
## 172: 10203.01 99.851 98.134 -0.03256180
## 173: 10201.66 99.854 98.189 -0.03200145
## 174: 10200.28 99.919 98.251 -0.03139937
## 175: 10198.90 99.949 98.296 -0.03091570
## 176: 10197.53 99.987 98.321 -0.03042133
## 
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 176 adjusted rate(s):
## Rate          : -0.04511041
## Adjustment    : -0.001068629
## Adjusted Rate : -0.04404178 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 176 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 175 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow     time
## 1:  NA    1     559.3172 -0.04511041 0.9980922      NA 166    210 10363.91
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10423.91 91.733 89.113 -0.04511041 -0.001068629   -0.04404178 -0.04404178
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.000512   NA 36 27 1.013253 -0.4698618
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -917.6989          NA  mgO2/hr/kg   -917.6989
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
66 8 CPRE375 CPRE377 Pretty patches 177 0.000512 ch1 Asus 0.04565 2023-04-18 2024-06-20 good/good 36 27 400.9746 0.205299 0.9842 917.6989 0.4698618 0.9980922 516.7242 0.2645628
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 229 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)